This repository contains the final improved version of my trading bot for the IMC Prosperity 3 Algorithmic Trading Challenge.
After the official competition ended, I continued working on optimizing and modularizing the strategy logic to better reflect my learning journey and demonstrate my algorithmic trading capabilities.
🌟 Grateful and excited to share my journey through the IMC Prosperity Challenge! 🌟
Coming in as a solo participant among 12,600+ teams, here’s what I achieved:
- 📈 256th Rank in India
- 🌍 1413th Rank Globally
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📘 Core trading models like:
- Black-Scholes Option Pricing
- Statistical Arbitrage
- Mean Reversion
- Pair Trading
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📊 Adapted strategies dynamically based on volatility regimes
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💪 Resilience: After a tough Round 4, I bounced back strong in Round 5!
This challenge has been an invaluable stepping stone in my journey into quantitative finance — and it's just the beginning!
I'm excited to keep exploring the world of algorithmic and quantitative trading. 🌐
| Asset | Strategy | Description |
|---|---|---|
KELP |
Bollinger Band Mean Reversion | Volatility-adaptive thresholds for long/short entries |
VOLCANIC_ROCK |
Mean Reversion | Rolling Bollinger bands + historical price memory |
VOLCANIC_ROCK_VOUCHERS |
Options Mean Reversion | ITM option strategies mirroring underlying; 10500 strike special logic |
SQUID_INK |
Extreme Move Detection | Reacts to sharp price deviations using recent volatility |
RAINFOREST_RESIN |
Market Making & Taking | Competitive quoting around fair value walls |
PICNIC_BASKETS |
ETF Arbitrage | Basket vs. component mispricing arbitrage |
DJEMBES |
Mean Reversion & Spread Adj. | Basic momentum and spread management |
CROISSANTS |
Informed Trader Tracking | Track Olivia's trades — best Sharpe trader in simulations |
MAGNIFICENT_MACARONS |
Sunlight-Driven Momentum | Trend-based trading based on sunlight index & TP/SL levels |
- Python 3
numpy,pandas,matplotlib,optuna,scikit-learn,jsonpickle
improved_trading_bot.py— final submission-ready versionREADME.md— this file